Ranjan Relan
AI and Data Strategy Consultant
Gurgaon, India
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Ranjan Relan is a AI and Data Strategy Consultant. He has more than 13+ years of experience in the field of data analytics and what he calls ABC of new technology i.e. AI, Big data and cloud computing. Ranjan Relan has advised multiple Fortune 100 firms on data strategy and adoption of AI. He also has advised and mentored education startups such as upgrad.com, udacity.com , HT funded Digiperform, etc.
Here are some of links of his speaking/mentoring/authoring course/talks etc.
-Author of "OPtimizing Microsoft AI solutions" course https://app.pluralsight.com/profile/author/ranjan-relan
-Industrial mentor for designing Upgrad.com course on PG Diploma on Big Data
-Mentor for Data Science – Udacity.com
-Writing book on AI/Machine Learning with BPB publishers (India’s largest publishing house for Computer Science books)
-Completed master equivalent course from ISB Hyderabad (Business Analytics)
-Guest Speaker: IIM Kashipur (India's top B school) (https://www.twipu.com/IIMKsp/tweet/1165150944956305408)
-Guest Speaker: IIM Jammu (India's top B school)
https://www.facebook.com/iimjammu/posts/data-is-the-new-oil-artificial-intelligence-is-the-new-electricityindustry-inter/3262654417094899/
Area of Expertise
Topics
Talent Strategy for Enabling AI and Digital Transformation in your Enterprise.
Talent Strategy for Enabling AI and Digital Transformation in your Enterprise.
In this talk we shall discuss what are the key roles which are required for implementing your AI and Data Strategy and if your company is planning to implement a large scale digital transformation projects. This session will be divided into four major parts:
Importance of defining Roles and Responsibilities
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We shall start by addressing why it is important to define roles for your organization considering talent shortage, niche technology and evolving landscape of roles for implementing AI and Data Strategy.
What are the Key Roles and Responsibilities
After discussing the importance of roles and responsibilities, we shall discuss the what are the key roles and responsibilities such as Data Analyst, Data Engineer, BI Engineer, Data Architect, Solution Architect, DevOps Engineer, MLOps Engineer, Data Scientist, Data Strategy Lead, Citizen Data Scientist, Data Testers , Chief Data Officer etc. In this we shall discuss what are the horizontal and vertical roles for e.g. Architect role should be an horizontal role with an eye on End to End data pipeline and ML pipeline ensuring security and guiding principle related to enterprise as well as best practices related to tools and frameworks leveraged in the project are followed. “Data Strategy Lead” is another horizont role which ensures there is end to end view of data assets, mapping of business outcomes to use cases, analytics and data sources. Similarly, vertical roles are the ones with deep tech expertise such as Data Engineers who would be focussing their codes follow the functional requirements, etc.
Operating Model and how key roles (AI and Data roles) fit into Scale Agile
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Next, we shall discuss the operating model which one needs to keep in mind where we shall pick up scaled agile principles and how it could be used for running your large AI and Data teams. We discuss what are the common problems which you could face while running large teams and how scaled agile could be used to iron out many issues.
GEAR-UP framework for filling the right talent for your Teams
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Finally, we shall go through the “GEAR-UP” framework which you could leverage to get the right talent. Here we shall discuss “GE:Get Skills from the market” via hiring, “R:Re Skill your existing legacy database engineers” and “Up:UpSkill” your team on new technology such as AI and Big data.
Mastering the Alchemy: Delving into the Art of Fine-Tuning LLMs
In this session we shall discuss what are the different strategies which are currently being developed in fine tuning LLMs (and reduce hallucinations) and deploying them using LLMOps which is still an active area of research. We shall discuss what complexities arises with fine tunning multi modal agents, advance techniques of fine tuning such as architecture modifications, transfer learning strategies and what are different ethical considerations which arises while fine tuning LLMs.
Evolution of Data Architecture
In this session, we shall discuss the evolution of data architectures. We shall start how in 1980's and 90's relational databases dominated with ER data modeling, followed by 2000's with MPP and star schema/snowflake schema. Then we shall discuss how the world moved to Big data where data lakes came into picture (2016's - data lake coined by Pentaho) to datalakehouse in 2020's and data mesh architecture. Overall session would be engaging explaning how with the rise of data products has though kept Functional Requirements changing but Non Functional Requirements changing at a much faster ratae.
Industrialization of Data Products using MLOps in Azure
Note: Ranjan Relan is an author and has published multiple courses (on Pluralsight and Coursera) on Azure including his course on MLOps (Optimizing Microsoft Azure AI solutions https://www.pluralsight.com/courses/microsoft-azure-optimizing-ai-solutions) which is a "partnership course with Microsoft" and is available as a linked reference in Microsoft Official Documentation.
About the Session
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In this session we shall discuss how data products which are powered by AI and machine learning algorithms can be industrialized using MLOps framework.
Challenges in industrialization of MLOps
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Product-ionizing machine learning algorithms comes with its different sets of complexities as compared to rule based DevOps where the output is constant.
Guiding Principle of MLOps
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We shall discuss what are the guiding principles of MLOps which includes how to ensure there is no change or minimal change in output, how to ensure there are no failures during model deployment, how to ensure data drift does not impacts the output,etc.
MLOPs & its Core Features
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We shall discuss what are the features of MLOps such as model versioning, data versioning, A/B testing, model deployment, continuous model monitoring, data drift,etc.
Enabling MLOps using Azure
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Finally, we shall discuss how Azure services can be leveraged for enabling MLOps for data products and what all features are provided.
AI and Data Strategy for Business Leaders
Session is dedicated for Entreprenurs, CXO's (CIO, CTO, Chief Data Officer, Chief Digital Officer, CEO, L&D) where we explain the importance of AI and data strategy for enabling their top line business initiatives for driving growth. Session has primarily three objectives:
In the first part, Session goes explains how AI & Data Strategy are two sides of the same coin, as quality, speed, context, security and governance of data can be an enabler as
well as hindrance in driving your AI initiatives.
Once we establish the importance of AI and Data Strategy for business, in the second part we will move onto how to reskill and upskill your workforce especially when it comes to Strategy for L&D and HR in what we call "ABC of New Technology" (AI, Big data and Cloud Computing) where we discuss landscape of AI and Cloud vendors
Finally, we will end with explaining the senior leadership in the audience on ethical issues in AI from how to eliminate AI bias, unemployment due to automation & how to guard your business from Artificial foolishness arising out of contextual information which is still to be deciphered by AI models
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